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Commitment Pacing for LPs: How to Build a Pacing Model That Survives Real Markets

Pacing decisions drive vintage diversification, which often matters more than manager selection. Here is how institutional LPs build, stress, and actually use a commitment pacing model.

Matthew S. · Allocator Desk · June 2026 · 9 min read

Most LP portfolios get into trouble not because of a single bad fund, but because commitments were sized for the wrong year. A pacing model is the discipline that prevents that. This is how institutional LPs build, stress, and actually use one.

Why pacing matters more than manager selection

The dispersion between top and bottom quartile vintage years often exceeds the dispersion between top and bottom quartile managers within a vintage. An LP who commits steadily through 2006, 2007, 2008, and 2009 ends up materially ahead of one who concentrated 80% of dollars in 2007, even if both picked the same managers. Pacing is the lever that controls vintage diversification, and vintage diversification is the lever that controls a portfolio's reliance on luck.

A pacing model answers three operational questions:

1. How much should we commit this year to hit our target private markets allocation? 2. What will our NAV, distributions, and unfunded commitments look like in three, five, and ten years? 3. What happens to all of the above if markets correct, distributions slow, or we miss a re-up?

Without a model, LPs end up either chronically under-allocated (the most common failure) or over-committed at the wrong moment (the more painful failure).

The core mechanic: contributions in, distributions out

A pacing model is a cash-flow simulator. For every fund commitment, you project:

• **Contributions** drawn down over the investment period (typically years 1 to 5)
• **Distributions** returned during the harvest period (typically years 4 to 10)
• **NAV** as the residual value at each point in time
• **Unfunded commitment** as committed minus called

Sum these across every existing fund and every assumed future commitment, and you have a portfolio-level cash flow forecast.

The simplest defensible model uses three inputs per fund:

• A **contribution curve** (e.g. Yale or Takahashi-Alexander style)
• A **distribution curve** (typically lagging contributions by 2-4 years)
• A **target net multiple** (the TVPI you expect)

You do not need a fancy model to start. A spreadsheet with year-by-year contribution and distribution percentages applied to each commitment will get you 80% of the value.

Setting the target allocation correctly

Pacing starts with a target, not a number of commitments. The target should be expressed as a percentage of the total portfolio (e.g. 20% private equity, 10% private credit) and translated into a dollar NAV target based on current and projected total assets.

Two traps to avoid:

• **Denominator drift**: if public markets fall 30%, your private allocation can mechanically jump from 20% to 28% without you committing a dollar. Pacing models should project the public side too, or at least stress-test it.
• **NAV lag**: private NAVs are reported with a quarter or two of delay, so your reported allocation always trails reality. Use a marked-forward NAV (current NAV plus an index proxy) for pacing decisions.

The pacing formula institutional LPs actually use

The most common starting point is the steady-state pacing formula:

> Annual commitment = Target NAV / Average fund life

If you want a $500M private equity NAV and the average fund life is 7 years, you commit roughly $500M / 7 = ~$71M per year. This is a useful first approximation but it ignores the J-curve, distribution timing, and re-up timing.

A better version factors in the contribution rate (the fraction of a commitment actually called at any time) and distribution drag:

> Annual commitment = (Target NAV - Current NAV - Expected net change) / Expected NAV-per-dollar-committed

In practice this lands at roughly 1.3x to 1.6x the steady-state number during a portfolio's build-out phase, and converges toward the steady-state number once the portfolio is mature.

Building the model: the four curves you need

1. Contribution curve: cumulative percent called by year. A reasonable buyout default is roughly 25% / 50% / 70% / 85% / 95% / 100% cumulative by end of years 1-6. 2. Distribution curve: cumulative percent returned by year. A typical buyout pattern starts in year 3-4 and accelerates through year 7-9. 3. NAV curve: derived from contributions minus distributions plus appreciation. NAV typically peaks around year 5-6 at 120-150% of called capital. 4. Re-up curve: probability that each GP raises a follow-on fund on a 3-4 year cycle. Important for steady-state pacing.

Different strategies need different curves. Venture has a longer J-curve and a back-loaded distribution profile. Credit is faster on both ends. Infrastructure draws over a longer window. Use strategy-specific curves; do not apply a single buyout curve to the whole book.

Stress testing: where pacing models earn their keep

A model that only shows the base case is dangerous. Run at least three scenarios every quarter:

• **Slow distributions**: extend the distribution curve by 18-24 months. Models the 2022-2024 exit environment.
• **Denominator effect**: assume public markets fall 25%. Watch what happens to your allocation percentage and your liquidity coverage.
• **Re-up miss**: drop two of your three largest GPs from the re-up schedule. Forces honest reflection on concentration risk.

The output you care about is not the central forecast. It is the range of unfunded commitment, range of net cash flow, and time to recover target allocation across scenarios. If any scenario forces you to skip a re-up you wanted to make, your base-case pacing is too aggressive.

The liquidity dimension that gets missed

Pacing is usually framed as an allocation problem. It is also a liquidity problem. Capital calls are non-negotiable and arrive on a GP's schedule, not yours. The model should track:

• **Peak unfunded commitment** as a percentage of total portfolio liquidity
• **Net cash flow** by year (contributions minus distributions)
• **Worst plausible 12-month cash draw** under stress

A common rule of thumb: keep peak unfunded below 35% of liquid assets, and ensure your worst-case 12-month net cash draw is covered by at least 2x in liquid securities. Endowments with spending obligations need wider buffers.

Re-ups, new managers, and the discipline question

Pacing models tend to assume re-ups happen on schedule. The harder question is what happens when a re-up window opens and you have to decide between:

• Re-upping at the same size to maintain pacing
• Cutting the check to reduce concentration
• Upsizing because conviction has grown
• Passing entirely

A good pacing process forces this conversation 12-18 months before the next fund opens, not three weeks before. The model gives you the dollar room; the IC gives you the decision.

How often to refresh

A pacing model is not a one-time build. Refresh:

• **Quarterly**: actual cash flows vs forecast, NAV updates, re-up calendar
• **Annually**: target allocation review, curve assumptions, strategy mix
• **Event-driven**: any major denominator move, any unexpected GP raise, any large distribution

LPs who refresh quarterly catch pacing drift early. LPs who refresh annually find out about it during the next downturn.

What good output looks like

A defensible quarterly pacing review fits on two pages:

• Page 1: current allocation, target allocation, gap, next 12 months commitment budget by strategy
• Page 2: 5-year NAV forecast under three scenarios, peak unfunded, net cash flow

Anything longer becomes a research artifact. The point is to drive commitment decisions, not to win a modeling award.

Common failure modes

• **Building once, never updating**: the most common failure. A pacing model is only useful if the assumptions are refreshed against actuals.
• **Using a single curve for all strategies**: venture, buyout, credit, and infra behave differently. Treating them the same understates risk.
• **Ignoring secondaries**: GP-led continuation vehicles and LP secondary sales now meaningfully reshape pacing. Model them explicitly.
• **Confusing pacing with selection**: pacing tells you how much to commit. Selection tells you to whom. A great pacing model with poor manager selection still loses; the inverse also fails because winners get under-sized.
• **Anchoring on peak vintages**: 2021-vintage marks are not a baseline. Adjust target multiples downward when modeling forward returns.

The minimum viable pacing model

If you do not have a pacing model today, the fastest path to a useful one:

1. List every existing commitment with size, vintage, strategy, called, NAV, distributed. 2. Apply strategy-specific contribution and distribution curves to project each fund through year 10. 3. Add a row for each assumed future commitment with the same curves. 4. Sum to portfolio level. Plot NAV, unfunded, and net cash flow by year. 5. Run the three stress scenarios above. Compare outputs.

This is a one-week build in a spreadsheet. Sophisticated LPs eventually move to dedicated tools, but the first version should be simple enough that the IC can interrogate every assumption.

The discipline behind the model

The model is the easy part. The hard part is committing the same amount in a year when the market feels expensive, your existing funds are calling capital faster than expected, and three GPs you respect are all raising at once. Pacing discipline means trusting the model when the environment is loud and re-examining the model when the environment is quiet. LPs who reverse that ordering end up over-allocated at peaks and under-allocated at troughs, which is the opposite of what a pacing model is built to prevent.

A pacing model does not predict returns. It does not pick managers. What it does is make sure that when the next great vintage opens, you have the room to commit, and when the next bad vintage closes, you have not over-committed into it. That alone is worth more than most of the analytical work that gets done on a private markets book.

Topics

• Commitment Pacing
• Portfolio Construction
• Vintage Diversification
• NAV Forecasting
• LP Operations